Optimal attention allocation: picking alpha or betting on beta?
研究了在信息容量约束下，投资者如何在多资产框架中分配注意力以最大化组合的阿尔法和贝塔收益，发现极端预测值会引发注意力竞争，且投资者技能随预测值大小在选股和择时之间切换。
We investigate a problem of attention allocation and portfolio selection with information capacity constraint and return predictability in a multi-asset framework. In a two-phase formulation, the optimal attention strategy maximizes the combined expected alpha payoffs and expected beta payoffs of the portfolio. Return predictors taking extreme values incentivize the investor to learn about them and this leads to competition among information sources for attention. Moreover, the investor trades with varying skills including picking alphas and betting on beta, depending on the magnitude of the related predictors. Our multi-period analysis using reinforcement learning demonstrates time-horizon effects on attention and investment strategies.